As I discussed yesterday, the Cisco VNI Usage Study is able to help translate the theoretical to the real, showcasing actual data sourced from a representative sampling of SP networks worldwide. Because it is primary data, we’re able to parse the information a number of different ways – we can effectively slice and dice it like a Ginsu – not just at a high level, but down to hourly granularity. When reconciled with the many time zones this study covers, it provides some great insights into user behavior across multiple geographic regions.

For example:

“Busy hour” traffic grew at a faster pace than average traffic, growing 41 percent since last year. Peak-hour Internet traffic is 72 percent higher than Internet traffic during an average hour. The ratio of the busy hour to the average hour increased from 1.59 to 1.72, globally. So, if you are a network planner, it’s not enough just to plan for the basic growth forecast of – you must account for the daily peaks as well. Based on our findings, that means planning for an overall capacity nearly ¾ more than the top-line traffic growth estimates in order to prevent massive congestion during daily peak periods.

Voice and video communications traffic is now six times higher than data communications traffic (email, instant messaging, instant messaging file transfer). Voice and video communications traffic (such as voice over IP [VoIP] and voice and video over instant messaging) has reached 2 percent of all traffic, up from less than 1 percent last year. With new tools applications becoming available seemingly every day, from Cisco ūmi to the latest from Skype, this will continue to be a space to watch.

Online video fluctuates more than file sharing traffic. Online video’s volatility (defined as the spread of traffic volume during the course of the day) is 51 percent higher than that of file sharing. The peak video hour is 91 percent higher than the average video hour, while the peak file sharing hour is 64 percent higher than the average file sharing hour. So while the move to online video is good for providers to showcase the quality of their networks compared to P2P where the role and value they can deliver is limited, it does provide more challenges such as the need to accommodate greater volatility.

IP traffic detail broken down hour-by-hour is provided below, as is a graph that not only shows the daily shifts in application usage, but also appeals to my fondness for pictures with colors – a veritable rainbow of broadband application diversity.

Please check it out and come back tomorrow, when we’ll investigate some of the web sites that are hosting these applications and popular content that continue to drive significant global Internet traffic growth.

Until then!

Application Traffic Share by Hour, 3QCY10

Viewing each application as a percentage of traffic provides a slightly different perspective. Online video exceeds P2P as a percentage of traffic most of the day, whereas P2P exceeds online video from midnight until 9 a.m. At its peak, P2P file sharing is 40 percent of traffic, and at its nadir, it is 16 percent of traffic, as the data in Table 4 shows. Video is 29 percent at its peak and 16 percent at its low point. While this may make the volatility of P2P file sharing seem comparatively high, the overall volume also needs to be taken into account. The traffic volume is lowest from midnight to 9 a.m., so the volume of P2P is in fact relatively constant throughout the day.

Hour

Data and Browsing

Online Video

P2P File Sharing

Other File Sharing

Voice and Video Communications

Data Communications

Gaming Consoles

PC Gaming

00:00:00

26.6%

27.0%

27.6%

16.9%

1.6%

0.2%

0.2%

0.5%

01:00:00

24.4%

24.1%

30.6%

19.2%

1.3%

0.2%

0.2%

0.6%

02:00:00

24.3%

22.2%

32.6%

19.3%

1.2%

0.2%

0.2%

0.5%

03:00:00

24.2%

19.9%

34.3%

20.0%

1.1%

0.2%

0.1%

0.7%

04:00:00

22.7%

17.7%

38.1%

20.3%

0.9%

0.2%

0.1%

0.7%

05:00:00

20.9%

15.5%

39.9%

21.8%

0.8%

0.2%

0.1%

1.4%

06:00:00

20.3%

16.7%

37.5%

23.1%

0.7%

0.2%

0.1%

1.9%

07:00:00

23.6%

18.8%

36.2%

20.8%

0.5%

0.3%

0.1%

0.3%

08:00:00

26.3%

20.5%

31.8%

20.4%

0.7%

0.3%

0.1%

0.4%

09:00:00

27.5%

20.5%

30.6%

20.0%

1.0%

0.4%

0.1%

0.3%

10:00:00

30.1%

24.2%

23.7%

20.1%

1.4%

0.5%

0.1%

0.4%

11:00:00

31.3%

25.1%

21.9%

19.2%

2.0%

0.4%

0.1%

0.6%

12:00:00

30.5%

26.0%

21.3%

19.5%

2.4%

0.4%

0.1%

0.4%

13:00:00

29.3%

26.5%

22.6%

19.1%

2.2%

0.4%

0.1%

0.4%

14:00:00

29.8%

26.6%

22.8%

18.5%

2.0%

0.4%

0.2%

0.3%

15:00:00

30.0%

27.7%

22.2%

17.7%

1.9%

0.3%

0.1%

0.5%

16:00:00

29.3%

27.7%

22.6%

18.1%

1.9%

0.3%

0.2%

0.4%

17:00:00

29.8%

27.6%

22.2%

18.0%

1.8%

0.3%

0.2%

0.7%

18:00:00

30.4%

27.2%

21.5%

18.5%

1.9%

0.3%

0.2%

0.6%

19:00:00

29.8%

27.7%

21.6%

18.8%

1.9%

0.3%

0.2%

0.4%

20:00:00

28.6%

27.5%

23.8%

17.7%

2.1%

0.2%

0.2%

0.4%

21:00:00

27.6%

28.3%

23.3%

18.2%

2.1%

0.2%

0.2%

0.7%

22:00:00

26.8%

28.8%

23.8%

17.8%

2.0%

0.2%

0.2%

1.0%

23:00:00

27.3%

28.9%

24.1%

16.9%

1.9%

0.2%

0.2%

1.1%

Source: VNI Usage, 2010

The graphic below illustrates the data in the table above. P2P file sharing can be classified as a “passive” application because it can generate significant traffic even in the absence of the user. As the VNI Forecast has pointed out in recent years, video has an emerging passive component as well, in the form of webcams and security cams, as well as Internet personal video recorders (PVRs). Should passive and ambient video become a force on the network, the video peak-to-average ratio could be nearly as low as P2P file sharing, although this is not likely to take place in the near future.

Thanks for the great data!
Could you still add the breakdown of total traffic volume per hour, i.e. how much of total traffic volume is generated at 00-01am, 01-02am, and so on? This cannot be calculated based on the numbers provided.

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